LINC00665 target let-7i/HMGA1 promotes the proliferation and invasion of hepatoma cells.

Objectives: Our group previously found that LINC00665 was upregulated in hepatocellular carcinoma (HCC) tissues through database analysis; however, the potential molecular mechanism of LINC00665 in HCC progression still needs further study.

Methods: qRTPCR was performed to determine the differential expression of LINC00665 and let-7i in HCC cells. Dual-luciferase reporter assays were performed to analyze the interaction of LINC00665 and let-7i.

Eliminating or Shortening the Calibration for a P300 Brain-Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study.

A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data.

Electromagnetic Source Imaging With a Combination of Sparse Bayesian Learning and Deep Neural Network.

Accurate reconstruction of the brain activities from electroencephalography and magnetoencephalography (E/MEG) remains a long-standing challenge for the intrinsic ill-posedness in the inverse problem. In this study, to address this issue, we propose a novel data-driven source imaging framework based on sparse Bayesian learning and deep neural network (SI-SBLNN). Within this framework, the variational inference in conventional algorithm, which is built upon sparse Bayesian learning, is compressed via constructing a straightforward mapping from measurements to latent sparseness encoding parameters using deep neural network.

Associating Latent Representations With Cognitive Maps via Hyperspherical Space for Neural Population Spikes.

Recently, there has been a focus on drawing progress on representation learning to obtain more identifiable and interpretable latent representations for spike trains, which helps analyze neural population activity and understand neural mechanisms. Most existing deep generative models adopt carefully designed constraints to capture meaningful latent representations. For neural data involving navigation in cognitive space, based on insights from studies on cognitive maps, we argue that the good representations should reflect such directional nature.

Enhancement of lower limb motor imagery ability dual-level multimodal stimulation and sparse spatial pattern decoding method.

Recently, motor imagery brain-computer interfaces (MI-BCIs) with stimulation systems have been developed in the field of motor function assistance and rehabilitation engineering. An efficient stimulation paradigm and Electroencephalogram (EEG) decoding method have been designed to enhance the performance of MI-BCI systems. Therefore, in this study, a multimodal dual-level stimulation paradigm is designed for lower-limb rehabilitation training, whereby visual and auditory stimulations act on the sensory organ while proprioceptive and functional electrical stimulations are provided to the lower limb.

Electromagnetic Source Imaging via Bayesian Modeling With Smoothness in Spatial and Temporal Domains.

Accurate reconstruction of cortical activation from electroencephalography and magnetoencephalography (E/MEG) is a long-standing challenge because of the inherently ill-posed inverse problem. In this paper, a novel algorithm under the empirical Bayesian framework, source imaging with smoothness in spatial and temporal domains (SI-SST), is proposed to address this issue. In SI-SST, current sources are decomposed into the product of spatial smoothing kernel, sparseness encoding coefficients, and temporal basis functions (TBFs).

Single-Trial EEG Classification via Orthogonal Wavelet Decomposition-Based Feature Extraction.

Achieving high classification performance is challenging due to non-stationarity and low signal-to-noise ratio (low SNR) characteristics of EEG signals. Spatial filtering is commonly used to improve the SNR yet the individual differences in the underlying temporal or frequency information is often ignored. This paper investigates motor imagery signals via orthogonal wavelet decomposition, by which the raw signals are decomposed into multiple unrelated sub-band components.

Learning Invariant Patterns Based on a Convolutional Neural Network and Big Electroencephalography Data for Subject-Independent P300 Brain-Computer Interfaces.

A brain-computer interface (BCI) measures and analyzes brain activity and converts this activity into computer commands to control external devices. In contrast to traditional BCIs that require a subject-specific calibration process before being operated, a subject-independent BCI learns a subject-independent model and eliminates subject-specific calibration for new users. However, building subject-independent BCIs remains difficult because electroencephalography (EEG) is highly noisy and varies by subject.

Capsule Network for ERP Detection in Brain-Computer Interface.

Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging.

Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation.

Objective: For the shared control systems, how to trade off the control weight between robot autonomy and human operator is an important issue, especially for BCI-based systems. However, most of existing shared controllers have paid less attention to the effects caused by subjects with different levels of brain control ability.

Approach: In this paper, a brain state evaluation network, termed BSE-NET, is proposed to evaluate subjects' brain control ability online based on quantized attention-gated kernel reinforcement learning.

Calcitonin gene-related peptide inhibits angiotensin II-induced NADPH oxidase-dependent ROS via the Src/STAT3 signalling pathway.

We had previously demonstrated that the calcitonin gene-related peptide (CGRP) suppresses the oxidative stress and vascular smooth muscle cell (VSMC) proliferation induced by vascular injury. A recent study also indicated that CGRP protects against the onset and development of angiotensin II (Ang II)-induced hypertension, vascular hypertrophy and oxidative stress. However, the mechanism behind the effects of CGRP on Ang II-induced oxidative stress is unclear.

Spatiotemporal-Filtering-Based Channel Selection for Single-Trial EEG Classification.

Achieving high classification performance in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often entails a large number of channels, which impedes their use in practical applications. Despite the previous efforts, it remains a challenge to determine the optimal subset of channels in a subject-specific manner without heavily compromising the classification performance. In this article, we propose a new method, called spatiotemporal-filtering-based channel selection (STECS), to automatically identify a designated number of discriminative channels by leveraging the spatiotemporal information of the EEG data.

EEG- and EOG-Based Asynchronous Hybrid BCI: A System Integrating a Speller, a Web Browser, an E-Mail Client, and a File Explorer.

This paper presents a new asynchronous hybrid brain-computer interface (BCI) system that integrates a speller, a web browser, an e-mail client, and a file explorer using electroencephalographic (EEG) and electrooculography (EOG) signals. More specifically, an EOG-based button selection method, which requires the user to blink his/her eyes synchronously with the target button's flashes during button selection, is first presented. Next, we propose a mouse control method by combining EEG and EOG signals, in which the left-/right-hand motor imagery (MI)-related EEG is used to control the horizontal movement of the mouse and the blink-related EOG is used to control the vertical movement of the mouse and to select/reject a target.

A Bayesian Shared Control Approach for Wheelchair Robot With Brain Machine Interface.

To enhance the performance of the brain-actuated robot system, a novel shared controller based on Bayesian approach is proposed for intelligently combining robot automatic control and brain-actuated control, which takes into account the uncertainty of robot perception, action and human control. Based on maximum a posteriori probability (MAP), this method establishes the probabilistic models of human and robot control commands to realize the optimal control of a brain-actuated shared control system. Application on an intelligent Bayesian shared control system based on steady-state visual evoked potential (SSVEP)-based brain machine interface (BMI) is presented for all-time continuous wheelchair navigation task.

Calcitonin gene-related peptide attenuates angiotensin II-induced ROS-dependent apoptosis in vascular smooth muscle cells by inhibiting the CaMKII/CREB signalling pathway.

Apoptosis is associated with various cardiovascular diseases. CGRP exerts a variety of effects within the cardiovascular system, and protects against the onset and development of angiotensin (Ang) II-induced vascular dysfunction and remodelling. However, it is not known whether CGRP has a direct effect on Ang II-induced apoptosis in vascular smooth muscle cells (VSMCs), and the mechanism underlying the anti-apoptotic role remains unclear.

Dilated-Inception Net: Multi-Scale Feature Aggregation for Cardiac Right Ventricle Segmentation.

Segmentation of cardiac ventricle from magnetic resonance images is significant for cardiac disease diagnosis, progression assessment, and monitoring cardiac conditions. Manual segmentation is so time consuming, tedious, and subjective that automated segmentation methods are highly desired in practice. However, conventional segmentation methods performed poorly in cardiac ventricle, especially in the right ventricle.

An Online Interactive Paradigm for P300 Brain-Computer Interface Speller.

For each brain-computer interface system, efficiency is a key issue that considers both accuracy and speed. The P300 spellers built upon oddball paradigm are usually less efficient due to the repetitive stimulation of multiple characters for reliable detection. In this paper, based on the online EEG signal, we propose an interactive paradigm for P300 speller to improve its efficiency, primarily focusing within the single characterP300 paradigm.

Spatial-Temporal Discriminative Restricted Boltzmann Machine for Event-Related Potential Detection and Analysis.

Detecting event-related potential (ERP) is a challenging problem because of its low signal-to-noise ratio and complex spatial-temporal features. Conventional detection methods usually rely on the ensemble averaging technique, which may eliminate subtle but important information in ERP signals and lead to poor detection performance. Inspired by the good performance of discriminative restricted Boltzmann machine (DRBM) in feature extraction and classification, we propose a spatial-temporal DRBM (ST-DRBM) to extract spatial and temporal features for ERP detection.

Bayesian Electromagnetic Spatio-Temporal Imaging of Extended Sources Based on Matrix Factorization.

Accurate estimation of the locations and extents of neural sources from electroencephalography and magnetoencephalography (E/MEG) is challenging, especially for deep and highly correlated neural activities. In this study, we proposed a new fully data-driven source imaging method, source imaging based on spatio-temporal basis function (SI-STBF), which is built upon a Bayesian framework, to address this issue. The SI-STBF is based on the factorization of a source matrix as a product of a sparse coding matrix and a temporal basis function (TBF) matrix, which includes a few TBFs.

Automatic System for Obstructive Sleep Apnea Events Detection Using Convolutional Neural Network.

Obstructive Sleep Apnea (OSA) is characterized by repetitive episodes of airflow reduction (hypopnea) or cessation (apnea), which, as a prevalent sleep disorder, can cause people to stop breathing for 10 to 30 seconds at a time and lead to serious problems such as daytime fatigue, impaired memory, and depression. This work intends to explore automatic detection of OSA events with 1-second annotation based on blood oxygen saturation, oronasal airflow, and ribcage and abdomen movements. Deep Learning (DL) technology, specifically, Convolutional Neural Network (CNN), is employed as a feature detector to learn the characteristics of the highorder correlation among visible data and corresponding labels.

Bilinear Regularized Locality Preserving Learning on Riemannian Graph for Motor Imagery BCI.

In off-line training of motor imagery-based brain-computer interfaces (BCIs), to enhance the generalization performance of the learned classifier, the local information contained in test data could be used to improve the performance of motor imagery as well. Further considering that the covariance matrices of electroencephalogram (EEG) signal lie on Riemannian manifold, in this paper, we construct a Riemannian graph to incorporate the information of training and test data into processing. The adjacency and weight in Riemannian graph are determined by the geodesic distance of Riemannian manifold.

A Hybrid Network for ERP Detection and Analysis Based on Restricted Boltzmann Machine.

Detecting and Please provide the correct one analyzing the event-related potential (ERP) remains an important problem in neuroscience. Due to the low signal-to-noise ratio and complex spatio-temporal patterns of ERP signals, conventional methods usually rely on ensemble averaging technique for reliable detection, which may obliterate subtle but important information in each trial of ERP signals. Inspired by deep learning methods, we propose a novel hybrid network termed ERP-NET.

Motor Imagery Classification Based on Bilinear Sub-Manifold Learning of Symmetric Positive-Definite Matrices.

In motor imagery brain-computer interfaces (BCIs), the symmetric positive-definite (SPD) covariance matrices of electroencephalogram (EEG) signals carry important discriminative information. In this paper, we intend to classify motor imagery EEG signals by exploiting the fact that the space of SPD matrices endowed with Riemannian distance is a high-dimensional Riemannian manifold. To alleviate the overfitting and heavy computation problems associated with conventional classification methods on high-dimensional manifold, we propose a framework for intrinsic sub-manifold learning from a high-dimensional Riemannian manifold.